Update README.md

README.md

@@ -3,12 +3,150 @@ base_model: FuseAI/FuseChat-Llama-3.1-8B-Instruct
 tags:
 - llama-cpp
 - gguf-my-repo
+license: llama3.1
 ---
 
 # Triangle104/FuseChat-Llama-3.1-8B-Instruct-Q8_0-GGUF
 This model was converted to GGUF format from [`FuseAI/FuseChat-Llama-3.1-8B-Instruct`](https://huggingface.co/FuseAI/FuseChat-Llama-3.1-8B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/FuseAI/FuseChat-Llama-3.1-8B-Instruct) for more details on the model.
 
+---
+Model details:
+-
+We present FuseChat-3.0, a series of models crafted to enhance 
+performance by integrating the strengths of multiple source LLMs into 
+more compact target LLMs. To achieve this fusion, we utilized four 
+powerful source LLMs: Gemma-2-27B-It, Mistral-Large-Instruct-2407, 
+Qwen-2.5-72B-Instruct, and Llama-3.1-70B-Instruct. For the target LLMs, 
+we employed three widely-used smaller models—Llama-3.1-8B-Instruct, 
+Gemma-2-9B-It, and Qwen-2.5-7B-Instruct—along with two even more compact
+ models—Llama-3.2-3B-Instruct and Llama-3.2-1B-Instruct. The implicit 
+model fusion process involves a two-stage training pipeline comprising 
+Supervised Fine-Tuning (SFT) to mitigate distribution discrepancies 
+between target and source LLMs, and Direct Preference Optimization (DPO)
+ for learning preferences from multiple source LLMs. The resulting 
+FuseChat-3.0 models demonstrated substantial improvements in tasks 
+related to general conversation, instruction following, mathematics, and
+ coding. Notably, when Llama-3.1-8B-Instruct served as the target LLM, 
+our fusion approach achieved an average improvement of 6.8 points across
+ 14 benchmarks. Moreover, it showed significant improvements of 37.1 and
+ 30.1 points on instruction-following test sets AlpacaEval-2 and 
+Arena-Hard respectively. We have released the FuseChat-3.0 models on Huggingface, stay tuned for the forthcoming dataset and code.
+
+
+
+	
+		
+	
+
+		Overview
+	
+
+
+
+Combining the strengths of multiple large language models (LLMs) 
+represents a promising approach to enhance individual model 
+capabilities. Model fusion is a technique that integrates the strengths 
+of robust source LLMs into a target LLM.
+
+
+Previous iterations of the FuseChat
+ series employed probabilistic distribution matrices generated by source
+ models to transfer knowledge to target models. We refer to this method 
+as explicit model fusion (EMF) because it involves a 
+well-defined knowledge transfer process. While applicable to models with
+ varying architectures and sizes, and without increasing memory overhead
+ during inference, this approach presents notable challenges such as 
+vocabulary alignment and the merging of distribution matrices from 
+different LLMs. These issues complicate model fusion, reduce its 
+efficiency, and may introduce noise and errors and affect the fusion 
+results.
+
+
+FuseChat-3.0, however, takes a different approach by enhancing a 
+single LLM through implicit learning from robust open-source LLMs, a 
+process we term implicit model fusion (IMF). The 
+concept of IMF has been widely utilized to improve the performance of 
+weaker models. For instance, a weak model can be boosted through 
+fine-tuning with outputs from stronger LLMs. Moreover, a reward model 
+can be trained using outputs from various LLMs, enabling it to learn and
+ capture the differences in capabilities between the LLMs. Zephyr 
+further collects responses from multiple LLMs and ranks them with GPT-4 
+to obtain preference data for training the policy. Inspired by recent 
+alignment techniques, we propose an IMF method to transfer the 
+capabilities of source LLMs to a target LLM through preference 
+optimization.
+
+
+Our IMF method follows a three-stage process aimed at effectively 
+transferring capabilities from source LLMs to a target LLM. First, 
+during dataset construction, we sample N responses from
+ each of the source LLMs and annotate these responses using an external 
+reward model. Second, in the supervised fine-tuning (SFT)
+ stage, we fine-tune the target model using the best responses, which 
+not only enhances the target model's capabilities but also helps 
+mitigate the distributional gap between the source and target models. 
+Finally, in the direct preference optimization (DPO) 
+stage, we optimize the target model by using the best and worst 
+responses from the source models as preference pairs, further enhancing 
+the target model's performance. The complete pipeline will be detailed 
+in the following paragraph.
+
+
+
+	
+		
+	
+
+		Dataset
+	
+
+
+
+
+	
+		
+	
+
+		Prompt Selection
+	
+
+
+
+Our datasets were designed to enhance model's instruction following, 
+general conversation, mathematics, coding, and Chinese-language 
+capabilities. We selected data from open-source community datasets, 
+applying targeted filtering and preprocessing. Key datasets and 
+filtering criteria included:
+
+
+Instruction Following & General Conversation: Sourced from UltraFeedback, Magpie-Pro-DPO-100K-v0.1, and HelpSteer2, excluding code and math data.
+Mathematics: Selected from OpenMathInstruct-2, with nearly 60,000 unique samples.
+Coding: Curated from leetcode and self-oss-instruct-sc2-exec-filter-50k, retaining prompts with test cases.
+Chinese Language: Integrated alpaca_gpt4_zh and Magpie-Qwen2-Pro-200K-Chinese, filtering out code and math prompts to retain approximately 10,000 high-quality samples.
+
+
+
+	
+		
+	
+
+		Response Sampling
+	
+
+
+
+For each dataset's prompts, we synthesized responses mainly from four different series of source models, specifically Gemma-2-27b-It, Mistral-Large-Instruct-2407, Qwen-2.5-72B-Instruct, and Llama-3.1-70B-Instruct.
+
+
+Instruction Following & General Conversation: We sampled each prompt five times from all the source models.
+Mathematics: We retained the responses generated by
+ Llama-3.1-405B-Instruct from the original dataset (OpenMathInstruct-2) 
+and additionally sampled responses using Qwen-2.5-Math-72B-Instruct.
+Coding: We sampled each prompt eight times for all source models.
+Chinese Language: We included single response sampled exclusively from Qwen-2.5-72B-Instruct.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)
 
@@ -47,4 +185,4 @@ Step 3: Run inference through the main binary.
 or 
 ```
 ./llama-server --hf-repo Triangle104/FuseChat-Llama-3.1-8B-Instruct-Q8_0-GGUF --hf-file fusechat-llama-3.1-8b-instruct-q8_0.gguf -c 2048
-```
+```